--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression - time-series-forecasting language: - en tags: - synthetic - healthcare - hospital-operations - operating-room - or-utilization - surgical-scheduling - staffing - workforce - nursing-shortage - equipment - biomedical - capacity-planning - bed-management - ed-boarding - aha - aorn - nsi - ecri - cms-conditions-of-participation - perioperative - case-cancellation - first-case-ontime - or-turnover - rn-vacancy - icu-occupancy pretty_name: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) size_categories: - 10K ⚠️ **PRIVACY & SYNTHETIC NATURE** > Every record in this dataset is **100% synthetic**. **No real patient data, no PHI, no real facility identifiers, no real surgeon or staff NPIs.** Population-level distributions match published AHA / AORN / NSI / ECRI benchmark sources but the facilities and operational events are computationally generated. --- ## What's in this sample | File | Rows | Cols | Description | |---|---|---|---| | `facilities.csv` | 3 | 11 | Facility master — type, teaching status, trauma level, bed count, OR suites, PACU bays, region | | `hospital_resources.csv` | 42 | 41 | Daily capacity + financial + quality KPIs per facility (14 days × 3 facilities) | | `or_schedule.csv` | ~4,200 | 18 | One row per surgical case — 22 case types, scheduled vs actual timing, cancellations, block ownership | | `staffing.csv` | ~13,500 | 11 | One row per staff-shift — 12 perioperative roles, OT/float/agency flags, staff-to-patient ratios | | `equipment.csv` | ~17,500 | 14 | One row per equipment-day — 18 equipment classes, utilization, downtime, maintenance schedule, repair cost | **Total:** ~3.9 MB across 6 files. --- ## Schema highlights ### `facilities.csv` (11 columns) — facility master `facility_id`, `facility_type` (academic / large / medium / small), `teaching_status` (Major Teaching / Minor Teaching / Non-Teaching), `trauma_level` (Level I-IV), `bed_count`, `icu_beds`, `or_suite_count`, `pacu_bays`, `state`, `region` (Northeast / Midwest / South / West), `daily_or_capacity` ### `hospital_resources.csv` (41 columns) — daily operational KPIs **Identity & temporal:** `facility_id`, `census_date`, `day_of_week`, `is_weekend` **Bed capacity:** `total_beds`, `occupied_beds`, `occupancy_rate`, `icu_beds_x`, `icu_occupied`, `icu_occupancy_rate`, `pacu_bays_x`, `pacu_patients`, `pacu_utilization_rate` **ED throughput:** `ed_boarding_hours`, `diversion_flag`, `diversion_hours`, `capacity_breach_flag`, `surge_day_flag` **OR financial & operational:** `or_utilization_rate`, `surgical_cases_scheduled`, `or_revenue_usd`, `or_cost_per_min_usd`, `total_or_minutes`, `contribution_margin_usd`, `block_release_efficiency` **Quality & safety:** `staffing_adequacy_score`, `operational_efficiency_index`, `surgical_site_infection_flag`, `near_miss_flag`, `consent_timeout_completed`, `equipment_safety_check_flag` ### `or_schedule.csv` (18 columns) — per-case scheduling `case_id`, `facility_id`, `case_date`, `or_id`, `case_type` (22 types: Orthopedic, Cardiac, General Surgery, Neurosurgery, OB/GYN, Urology, ENT, Plastic Surgery, Vascular, Thoracic, Transplant, Trauma, Ophthalmology, Colorectal, Bariatric, Endoscopy, Interventional Radiology, Gynecologic Oncology, Pediatric Surgery, Spinal, Hand Surgery, Robotic Assisted), `surgeon_id`, `is_emergency`, `scheduled_start_min`, `actual_start_min`, `start_delay_min`, `first_case_ontime_flag`, `scheduled_duration_min`, `actual_duration_min`, `turnover_time_min`, `cancellation_flag`, `cancellation_reason`, `block_owner`, `add_on_flag` ### `staffing.csv` (11 columns) — daily shift records `shift_id`, `facility_id`, `shift_date`, `staff_id`, `staff_role` (12 roles: Surgeon, Anesthesiologist, CRNA, Scrub Tech, RN Circulator, PA/NP, Resident, Pharmacist, Radiology Tech, Biomedical Tech, Environmental Services, Unit Coordinator), `shift_type` (Day / Evening / Night), `hours_worked`, `overtime_flag`, `float_pool_flag`, `agency_flag`, `staff_to_patient_ratio` ### `equipment.csv` (14 columns) — daily equipment utilization `asset_id`, `facility_id`, `record_date`, `equipment_class` (18 classes including Anesthesia Machine, Patient Monitor, Infusion Pump, Electrosurgical Unit, Sterilization Autoclave, CT Scanner, MRI Scanner, C-Arm Fluoroscopy, Endoscope Processor, Intraoperative MRI, ECMO Circuit, CRRT Machine, Cardiac Cath Lab Equipment, Defibrillator, Ventilator, Robotic Surgical System, Imaging Workstation, Hybrid OR Imaging), `equipment_age_yrs`, `utilization_rate`, `in_service_hours`, `downtime_hours`, `unplanned_downtime_flag`, `downtime_cause` (Hardware Failure / Software Error / Power Surge / User Error / Calibration Failure / Component Wear / Connectivity Issue / Sensor Malfunction), `last_maintenance_date`, `next_maintenance_due`, `failure_flag`, `repair_cost_usd` --- ## Calibration source story The full HLT-010 generator anchors all distributions to authoritative hospital operations references: - **AHA Annual Survey 2023** (American Hospital Association) — OR utilization (78.4%), case cancellations (8.2%), bed occupancy (81.2%), ED boarding (3.2hr), revenue per case (~$18,400) - **AORN Benchmarks** (Association of periOperative Registered Nurses) — first-case on-time start (82%), OR turnover (28 ± 8 min), surgical tech vacancy (22.8%) - **NSI Nursing Solutions 2023** — RN vacancy rate (15.6%), turnover patterns - **ECRI Institute** — Equipment unplanned downtime (~4.2%), age-related failure curves - **CMS Conditions of Participation** — ICU occupancy target max 85%, staffing-to-patient ratios - **IHI (Institute for Healthcare Improvement)** — Operational efficiency benchmarks, surge capacity ### Sample-scale validation scorecard | Metric | Observed | Target | Tolerance | Status | Source | |---|---|---|---|---|---| | OR utilization rate | 71.3% | 70% | ±10% | ✅ PASS | AHA 2023 | | Case cancellation rate | 7.6% | 8% | ±3% | ✅ PASS | AHA 2023 | | First-case on-time rate | 84.5% | 82% | ±8% | ✅ PASS | AORN Benchmarks | | OR turnover (min) | 27.4 | 28.0 | ±4.0 | ✅ PASS | AORN | | Bed occupancy rate | 81.7% | 78% | ±10% | ✅ PASS | AHA 2023 | | ED boarding hours (mean) | 3.28 | 3.2 | ±1.2 | ✅ PASS | AHA 2023 | | ICU occupancy (under CMS max) | 80.7% | ≤85% | — | ✅ PASS | CMS CoP | | Equipment downtime rate | 5.1% | 4.8% | ±1.8% | ✅ PASS | ECRI Institute | | Case type diversity | 22 | 22 | ±2 | ✅ PASS | AORN surgical taxonomy | | Staff role diversity | 12 | 12 | — | ✅ PASS | AORN team composition | **Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).** --- ## Loading examples ### Pandas — explore the operational data ```python import pandas as pd facilities = pd.read_csv("facilities.csv") capacity = pd.read_csv("hospital_resources.csv", parse_dates=["census_date"]) ors = pd.read_csv("or_schedule.csv", parse_dates=["case_date"]) staffing = pd.read_csv("staffing.csv", parse_dates=["shift_date"]) equipment = pd.read_csv("equipment.csv", parse_dates=["record_date"]) # OR utilization by facility type print(capacity.merge(facilities, on="facility_id") .groupby("facility_type")["or_utilization_rate"] .agg(["mean", "std", "min", "max"]).round(3)) # Case type mix print(ors["case_type"].value_counts(normalize=True).head(10).round(3)) # Cancellation reasons print(ors.loc[ors["cancellation_flag"] == True, "cancellation_reason"] .value_counts()) ``` ### Hugging Face Datasets ```python from datasets import load_dataset ds = load_dataset("xpertsystems/hlt010-sample", data_files={ "facilities": "facilities.csv", "hospital_resources": "hospital_resources.csv", "or_schedule": "or_schedule.csv", "staffing": "staffing.csv", "equipment": "equipment.csv", }) print(ds) ``` ### OR utilization forecasting baseline ```python import pandas as pd from sklearn.ensemble import GradientBoostingRegressor from sklearn.model_selection import train_test_split cap = pd.read_csv("hospital_resources.csv", parse_dates=["census_date"]) cap["month"] = cap["census_date"].dt.month cap["dayofweek_num"] = cap["census_date"].dt.dayofweek features = ["bed_count", "or_suite_count", "is_weekend", "dayofweek_num", "month", "occupancy_rate", "icu_occupancy_rate", "surgical_cases_scheduled"] X = cap[features].fillna(0) y = cap["or_utilization_rate"] Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.3, random_state=42) m = GradientBoostingRegressor(random_state=42).fit(Xtr, ytr) print(f"OR utilization R²: {m.score(Xte, yte):.3f}") ``` ### Equipment maintenance prediction ```python import pandas as pd eq = pd.read_csv("equipment.csv", parse_dates=["record_date"]) # Downtime rate by equipment age eq["age_bucket"] = pd.cut(eq["equipment_age_yrs"], [0, 3, 6, 10, 15], labels=["0-3yr", "3-6yr", "6-10yr", "10-15yr"]) print(eq.groupby("age_bucket")["unplanned_downtime_flag"].mean().round(3)) # Repair cost distribution print(eq.loc[eq["repair_cost_usd"] > 0, "repair_cost_usd"].describe()) ``` ### Staffing analysis ```python import pandas as pd staff = pd.read_csv("staffing.csv") # Overtime rate by role print(staff.groupby("staff_role")["overtime_flag"].mean().sort_values(ascending=False)) # Agency staff reliance print(staff.groupby(["facility_id", "staff_role"])["agency_flag"] .mean().unstack().round(3)) ``` --- ## Suggested use cases - **OR utilization forecasting** — predict next-day OR utilization from facility characteristics + recent operational patterns - **Surgical case cancellation prediction** — classify cancellation risk to enable proactive intervention - **Block schedule optimization** — analyze block release efficiency and underutilized blocks - **Equipment failure prediction** — predict `unplanned_downtime_flag` from age + utilization + maintenance history - **Maintenance scheduling optimization** — risk-adjusted preventive maintenance interval modeling - **Staffing-to-acuity matching** — analyze `staff_to_patient_ratio` × `acuity` patterns for nurse scheduling - **Overtime / agency cost modeling** — predict overtime hours and agency staffing needs - **Bed capacity surge prediction** — predict `surge_day_flag` and `diversion_flag` from upstream factors - **ED boarding root cause analysis** — relate `ed_boarding_hours` to ICU occupancy and discharge patterns - **Quality & safety event modeling** — predict near-miss / SSI / consent timeout events from staffing + acuity - **Financial contribution margin modeling** — analyze contribution margin drivers across facility types - **Hospital ML pretraining** — pretrain operational forecasting models before fine-tuning on real EHR/EMR data - **Operations research education** — perioperative scheduling, queueing theory, capacity planning coursework --- ## Sample vs. full product | Aspect | This sample | Full HLT-010 product | |---|---|---| | Facilities | 3 (mixed) | 50+ (default) up to 500+ | | Time window | 14 days | 365+ days (multi-year configurable) | | Facility types | Mixed (3) | Mixed / academic-only / community-only / critical_access | | Output format | CSV | CSV / Parquet / JSON | | Schema | identical | identical | | Calibration | identical | identical | | License | CC-BY-NC-4.0 | Commercial license | The full product unlocks: - **Up to 500+ facilities** for system-wide operations modeling - **Multi-year longitudinal windows** for trend analysis and intervention impact studies - **Configurable facility mix** for targeted segmentation (academic-only / community-only / CAH) - **Parquet output** for production data pipelines - Commercial use rights **Contact us for the full product.** --- ## Limitations & honest disclosures - **Sample is preview-only.** 3 facilities × 14 days × ~35K operational records is enough to demonstrate schema and calibration, but is **not statistically sufficient** for facility-level capacity planning models or season-aware forecasting. Use the full product (50+ facilities × 365 days) for serious work. - **Sample includes 3 facility types (academic + large + medium), not all 4.** The `critical_access` facility type is not represented at n=3 due to random sampling. The full product reliably covers all 4 types. - **OR utilization runs slightly below the headline AHA target.** Sample mean is ~70% vs AHA 78.4% pure target. This is partly because mixed facility_mix includes community facilities (which average lower OR utilization) and partly small-N effects at 3 facilities × 14 days. The full product hits the AHA target at scale. - **Equipment downtime runs slightly elevated (5.1% vs ECRI 4.2%).** The generator's age-based `failure_multiplier` produces realistic but somewhat-higher-than-target downtime for aging assets. Reflects real-world equipment fleet aging — production hospitals with younger fleets see lower rates. - **PACU utilization clips at 1.0.** The generator caps PACU utilization at 100% rather than allowing over-capacity. At busy academic centers, real PACU congestion exceeds capacity (queue forms) — this is hidden by the cap. - **Staff IDs are synthetic random integers.** No real NPIs, no real practitioner identifiers. Surgeon IDs are equally synthetic. - **Equipment IDs are synthetic identifiers**, not real GUDID device IDs. - **Block-schedule data is daily-aggregated, not minute-level.** The full product can be extended with minute-level block scheduling for highly-detailed OR room optimization. - **No real ICD-10 / CPT case data joins.** Case types are categorical groupings (Orthopedic, Cardiac, etc.) — the full ICD-10/CPT/MS-DRG joins are in the companion HLT-005 hospital admission dataset. - **Synthetic, not derived from real hospital operations data.** Distributions match published AHA/AORN/NSI/ECRI references but do NOT reflect any specific real hospital. --- ## Ethical use guidance This dataset is designed for: - Hospital operations analytics methodology development - OR scheduling and capacity planning research - Equipment maintenance prediction ML - Nursing workforce analytics - ED throughput optimization research - Healthcare AI pretraining for operational forecasting - Educational use in hospital operations management and operations research This dataset is **not appropriate for**: - Making real staffing decisions about real personnel - Real surgeon performance evaluation - Real equipment retirement/procurement decisions without validation - Discriminatory analyses targeting protected demographic groups - Hospital quality scoring or pay-for-performance modeling without real-data validation --- ## Companion datasets in the Healthcare vertical - [HLT-001](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated) - [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles) - [HLT-003](https://huggingface.co/datasets/xpertsystems/hlt003-sample) — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep) - [HLT-004](https://huggingface.co/datasets/xpertsystems/hlt004-sample) — Synthetic Disease Progression Dataset (NSCLC + Heart Failure longitudinal) - [HLT-005](https://huggingface.co/datasets/xpertsystems/hlt005-sample) — Synthetic Hospital Admission Dataset (5K admissions + bed utilization) - [HLT-006](https://huggingface.co/datasets/xpertsystems/hlt006-sample) — Synthetic Medical Imaging Dataset (1K studies + COCO annotations + reports) - [HLT-007](https://huggingface.co/datasets/xpertsystems/hlt007-sample) — Synthetic Drug Response Dataset (3K patient-treatments × 25 drug classes + PGx + PK) - [HLT-008](https://huggingface.co/datasets/xpertsystems/hlt008-sample) — Synthetic Healthcare Claims Dataset (500 members + 30K claims + fraud labels) - [HLT-009](https://huggingface.co/datasets/xpertsystems/hlt009-sample) — Synthetic Continuous Vital Sign Monitoring Dataset (25 ICU episodes + alarms) - **HLT-010** — Synthetic Hospital Resource Usage Dataset (you are here) Use **HLT-001 through HLT-010 together** for the complete healthcare data stack: clinical (population/EHR/trials/progression) + operational (admissions/imaging/pharma/claims/monitoring/**resources**) — 10 datasets covering every major workflow in the modern hospital. --- ## Citation If you use this dataset, please cite: ```bibtex @dataset{xpertsystems_hlt010_sample_2026, author = {XpertSystems.ai}, title = {HLT-010 Synthetic Hospital Resource Usage Dataset (Sample Preview)}, year = 2026, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/xpertsystems/hlt010-sample} } ``` --- ## Contact - **Web:** [https://xpertsystems.ai](https://xpertsystems.ai) - **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) - **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) **Full product License:** Commercial — please contact for pricing.